Self-Supervised Difference Detection for Refinement CRF and Seed Interpolation
Abstract
To minimize annotation costs associated with training of semantic segmentation models, weakly-supervised segmentation approaches have been extensively studied. In this paper, we propose a novel method: Self-Supervised Difference Detection (SSDD) module which evaluates confidence of each of the pixels of segmentation masks and integrate highly confident pixels of two candidate masks.
Cite
Text
Shimoda and Yanai. "Self-Supervised Difference Detection for Refinement CRF and Seed Interpolation." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2019.Markdown
[Shimoda and Yanai. "Self-Supervised Difference Detection for Refinement CRF and Seed Interpolation." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2019.](https://mlanthology.org/cvprw/2019/shimoda2019cvprw-selfsupervised/)BibTeX
@inproceedings{shimoda2019cvprw-selfsupervised,
title = {{Self-Supervised Difference Detection for Refinement CRF and Seed Interpolation}},
author = {Shimoda, Wataru and Yanai, Keiji},
booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
year = {2019},
url = {https://mlanthology.org/cvprw/2019/shimoda2019cvprw-selfsupervised/}
}